Science influence on Twitter

Primary Followers: These are simply the number of people following a given tweep.

Second Order Followers: These are the number of people following the followers of the original tweep.  In principle, second order followers gives an idea of how much reach a person has – if they post a tweet, and it is re-tweeted, how many people could it potentially reach.  The indicator is flawed as it doesn’t account for duplicate second-order followers, but it gives a rough impression of how influential a person might be.

Social Capital: This is simply the average number of people following each of a tweep’s followers.  The indicator is used by the folks at Twinfluence.com as a way of gauging the overall influence of a person’s followers – the higher your social capital, the more impact you are likely to have.  Again, it’s a flawed indicator, as a person with two or three followers who in turn have a high following ends up with a very high social capital index.  But it does give a different perspective on someone’s potential impact.

I’m still not sure what – if anything – this analysis really shows.  But there are some interesting features.  Perhaps must importantly, it’s clear that the indicated influence of someone changes radically, according to how it is measured.  Taking @2020science as an example, I have a reasonable-sized bubble on the Primary Followers chart, but disappear into obscurity on the Social Capital Chart.  What is also clear is that if social capital is a good measure of influence (and I’m not entirely convinced that it is yet), you don’t have to have a huge following to be a key player on Twitter. I would emphasize strongly that this this analysis shouldn’t be taken too seriously from a personal perspective – Twitter is a tool that should be used in whatever way works best for you,  irrespective of rather flawed ranking systems.  However, the analysis does provide insight into the Twitter community as a whole.  In this particular case, not much can be inferred from a single data point.  But if the same evaluation of the same group of people is carried out at regular intervals – say, every four months or so – it should be possible to chart the evolution of Twitter as a social medium for science communication. One final thought.  In crunching the figures for this analysis, I was struck by an apparent lack of correlation between primary followers, secondary followers and social capital.  Here are all three plotted together:

What you see is social capital on the horizontal axis, second order followers on the vertical axis, with the size of the points reflecting the number of primary followers.  In among the rather scattered data, there are some interesting qualitative trends – high social capital does not associate well with high second order followers and, while there is some association between primary and second order followers, this isn’t always the case.  It emphasizes again that influence depends on how you measure it!

Update 4/23/09: It’s a little flaky, but here’s a YouTube demo on playing around with the bubble chart.

Notes:

For this analysis, I knocked out any users with no followers, and two tweeps with excessively high followings (@guardiantech and @Astronautics).  @BILL_ROMANOS had so many second order followers that the number was capped at 20,000,000

My thanks to David Bradley for compiling the list of “Scientific Twitter Friends” in the first place.  This is largely a self-selected list of science-types on Twitter, and in no way represents the full scientific community there.  But it does provide a highly useful cohort of people who profess to have a science-perspective, and can be tracked over time.

And finally, many thanks to @ruthseeley for suggesting the indicators of influence given by Twinfluence.  My fingers may take longer to thank you – the analysis was a long and tedious one – but I think it was worth while!